ETACM: an encoded-texture active contour model for image segmentation with fuzzy boundaries

Ranjbarzadeh, Ramin; Sadeghi, Soroush; Fadaeian, Aida; Ghoushchi, Saeid Jafarzadeh; Tirkolaee, Erfan Babaee; Caputo, Annalina; Bendechache, Malika

doi:10.1007/s00500-023-08983-3

Cited by 11 publications

(1 citation statement)

References 71 publications

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“…This paper utilizes deep learning technology to enhance the selection of emotional design through the separation of appearance and color. Deep learning technology can solve many problems, such as object detection, image classification, image segmentation (Ranjbarzadeh & Tirkolaee, 2023), and image generation, and has been widely applied in various industries (Weber, Arabnia, Aydın, Tirkolaee, 2023;Song, Li, et al, 2022;Wu, 2022;Morales & Suárez-Rocha, 2022;Dayyala, 2022). Deep learning (Miao & Ruomu, 2023;Zhou & Fang, 2022) is a machine learning subfield originating from the study of artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Emotion Generation Model Combining CGAN and pix2pix

Wang,

Ghani,

Zhou

2023

Journal of Organizational and End User Computing

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Computer vision has made significant advancements in emotional design. Designers can now utilize computer vision to create emotionally captivating designs that deeply resonate with people. This article aims at enhancing emotional design selection by separating appearance and color. A two-stage emotional design method is proposed, which yields significantly better results compared to classical single-stage methods.. In the Radboud face dataset (RaFD), facial expressions primarily rely on appearance, while color plays a relatively smaller role. Therefore, the two-stage model presented in this article can focus on shape design. By utilizing the SSIM image quality evaluation index, our model demonstrates a 31.63% improvement in generation performance compared to the CGAN model. Additionally, the PSNR image quality evaluation index shows a 10.78% enhancement in generation performance. The proposed model achieves superior design results and introduces various design elements.This article exhibits certain improvements in design effectiveness and scalability compared to conventional models.

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